{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import tarfile\ndef extract(tar_file, path):\n    opened_tar = tarfile.open(tar_file)\n     \n    if tarfile.is_tarfile(tar_file):\n        opened_tar.extractall(path)\n    else:\n        print(\"The tar file you entered is not a tar file\")\n        \n# extract('../input/stanford-car-dataset-full/car_ims.tar', '/kaggle/working/data/')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install console-progressbar","execution_count":2,"outputs":[{"output_type":"stream","text":"Requirement already satisfied: console-progressbar in /opt/conda/lib/python3.7/site-packages (1.1.2)\r\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport scipy.io as sio \nimport os\nimport shutil\nimport cv2\nimport matplotlib.pyplot as plt\nimport random\nfrom console_progressbar import ProgressBar","execution_count":3,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Matching between class number and make name"},{"metadata":{"trusted":true},"cell_type":"code","source":"os.listdir('../input/')","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"['stanford-cars-dataset', 'mitsub', 'devkitt', 'resnextmod', 'devkit']"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_make(class_name):\n    idx = class_name.find(' ')\n    return class_name[:idx]\n\nmeta = sio.loadmat('../input/stanford-cars-dataset/cars_annos.mat')\nmake_list = []\nconvert = {}\n\nnumber_to_make = {}\nfor i, clas in enumerate(meta['class_names'][0]):\n    \n    make = get_make(clas[0])\n    if make not in make_list:\n        make_list.append(make)\n    convert[i] = make_list.index(make)\n    number_to_make[convert[i]] = make","execution_count":6,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"number_to_make[convert[192]]","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"'Volvo'"},"metadata":{}}]},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"def get_labels(path):\n    annos = sio.loadmat('../input/devkit/devkit/cars_train_annos.mat')\n#     annos = sio.loadmat('../input/stanford-cars-dataset/cars_annos.mat')\n    _, total_size = annos[\"annotations\"].shape\n    labels = {}\n    for i in range(total_size):\n        path = annos['annotations'][:,i][0][5][0]\n        clas = annos['annotations'][:,i][0][4][0][0]\n        labels[path] = convert[clas-1]\n    return labels\nlabels_n= get_labels('cars_train_annos')","execution_count":8,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(len(labels_n.keys()))","execution_count":9,"outputs":[{"output_type":"stream","text":"8144\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# Pre-Processing"},{"metadata":{"trusted":true},"cell_type":"code","source":"# for given filenames, labels and boxes function split\n# data into validation and training data, cropping bounding boxes\n# resizing and saving to a folder, if it's test data we don't have labels and split data\ndef save_data(fnames, labels, bboxes, data_type='train'):\n    if data_type == 'train':\n        src_folder = '../input/stanford-cars-dataset/cars_train/cars_train/'\n    else:\n        src_folder = '../input/stanford-cars-dataset/cars_test/cars_test'\n    num_samples = len(fnames)\n    \n    if data_type == 'train':\n        perm = np.random.permutation(num_samples)\n\n        train_split = 0.8\n        num_train = int(round(num_samples * train_split))\n\n    pb = ProgressBar(total=100, prefix='Save train data', suffix='', decimals=3, length=50, fill='=')\n\n    for i in range(num_samples):\n        fname = fnames[i]\n        if data_type == 'train':\n            label = labels[i]\n        (x1, y1, x2, y2) = bboxes[i]\n\n        src_path = os.path.join(src_folder, fname)\n        src_image = cv2.imread(src_path)\n        height, width = src_image.shape[:2]\n        # margins of 16 pixels\n        margin = 16\n        x1 = max(0, x1 - margin)\n        y1 = max(0, y1 - margin)\n        x2 = min(x2 + margin, width)\n        y2 = min(y2 + margin, height)\n        # print(\"{} -> {}\".format(fname, label))\n        pb.print_progress_bar((i + 1) * 100 / num_samples)\n        dst_path = os.path.join('/kaggle/working/data/test', fname)\n        if data_type == 'train':\n            if i < num_train:\n                dst_folder = '/kaggle/working/data/train/'\n            else:\n                dst_folder = '/kaggle/working/data/valid/'\n\n            dst_path = os.path.join(dst_folder, label)\n            if not os.path.exists(dst_path):\n                os.makedirs(dst_path)\n            dst_path = os.path.join(dst_path, fname)\n\n        crop_image = src_image[y1:y2, x1:x2]\n        dst_img = cv2.resize(src=crop_image, dsize=(224, 224))\n        cv2.imwrite(dst_path, dst_img)        ","execution_count":10,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# parsing labels: class indexes, bounding boxes, file names\ndef process_data(data_type = 'train'):\n    print(\"Processing train data...\")\n    if data_type == 'train':\n        cars_annos = sio.loadmat('../input/devkit/devkit/cars_train_annos.mat')\n    else:\n        cars_annos = sio.loadmat('../input/stanford-cars-dataset/car_devkit/devkit/cars_test_annos.mat')\n    annotations = cars_annos['annotations']\n    annotations = np.transpose(annotations)\n\n    fnames = []\n    class_ids = []\n    bboxes = []\n    labels = []\n\n    for annotation in annotations:\n        bbox_x1 = annotation[0][0][0][0]\n        bbox_y1 = annotation[0][1][0][0]\n        bbox_x2 = annotation[0][2][0][0]\n        bbox_y2 = annotation[0][3][0][0]\n        \n        if data_type == 'train':\n            class_id = annotation[0][4][0][0]\n            fname = annotation[0][5][0]\n            labels.append('%02d' % labels_n[fname])\n            class_ids.append(labels_n[fname])\n            \n        else:\n            fname = annotation[0][4][0]\n        \n        bboxes.append((bbox_x1, bbox_y1, bbox_x2, bbox_y2))\n        fnames.append(fname)\n        \n    if data_type == 'train':\n        labels_count = np.unique(class_ids).shape[0]\n        print(np.unique(class_ids))\n        print('The number of different cars is %d' % labels_count)\n    else:\n        print('Test data processing')\n    save_data(fnames, labels, bboxes, data_type)","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def make_folder(folder):\n    if not os.path.exists(folder):\n        os.makedirs(folder)","execution_count":12,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"new_dirs = ['/kaggle/working/data/train', '/kaggle/working/data/valid', '/kaggle/working/data/test']\n\nfor new_dir in new_dirs:\n    make_folder(new_dir)","execution_count":13,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"process_data('train')","execution_count":14,"outputs":[{"output_type":"stream","text":"Processing train data...\n[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n 48]\nThe number of different cars is 49\nSave train data |==================================================| 100.000% \n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"process_data('test')","execution_count":15,"outputs":[{"output_type":"stream","text":"Processing train data...\nTest data processing\nSave train data |==================================================| 100.000% \n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def del_dirs(dirs_list):\n    for dirr in dirs_list:\n        shutil.rmtree(dirr)\n# del_dirs(new_dirs)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Creating data transformations, data loaders"},{"metadata":{"trusted":true},"cell_type":"code","source":"from __future__ import print_function, division\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nimport numpy as np\nimport torchvision\nfrom torchvision import datasets, models, transforms\nimport matplotlib.pyplot as plt\nimport time\nimport os\nimport copy\n\nplt.ion()   # interactive mode","execution_count":16,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"bs = 64\n\n# Data augmentation and normalization for training\n# Just normalization for validation\ndata_transforms = {\n    'train': transforms.Compose([\n        transforms.RandomResizedCrop(224),\n        transforms.RandomHorizontalFlip(),\n        transforms.ToTensor(),\n        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n    ]),\n    'valid': transforms.Compose([\n        transforms.Resize(256),\n        transforms.CenterCrop(224),\n        transforms.ToTensor(),\n        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n    ]),\n}\n\ndata_dir = 'data/'\nimage_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n                                          data_transforms[x])\n                  for x in ['train', 'valid']}\ndataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=bs,\n                                             shuffle=True, num_workers=4)\n              for x in ['train', 'valid']}\ndataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'valid']}\nclass_names = image_datasets['train'].classes\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")","execution_count":17,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def imshow(inp, title=None):\n    \"\"\"Imshow for Tensor.\"\"\"\n    inp = inp.numpy().transpose((1, 2, 0))\n    mean = np.array([0.485, 0.456, 0.406])\n    std = np.array([0.229, 0.224, 0.225])\n    inp = std * inp + mean\n    inp = np.clip(inp, 0, 1)\n    plt.imshow(inp)\n    if title is not None:\n        plt.title(title)\n    plt.pause(0.001)  # pause a bit so that plots are updated\n\n\n# Get a batch of training data\ninputs, classes = next(iter(dataloaders['train']))\n\n# Make a grid from batch\nout = torchvision.utils.make_grid(inputs)\nimshow(out, title=[class_names[x] for x in classes])","execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"# Function for model training"},{"metadata":{"trusted":true},"cell_type":"code","source":"def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n    since = time.time()\n\n    best_model_wts = copy.deepcopy(model.state_dict())\n    best_acc = 0.0\n\n    for epoch in range(num_epochs):\n        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n        print('-' * 10)\n\n        # Each epoch has a training and validation phase\n        for phase in ['train', 'valid']:\n            if phase == 'train':\n                model.train()  # Set model to training mode\n            else:\n                model.eval()   # Set model to evaluate mode\n\n            running_loss = 0.0\n            running_corrects = 0\n\n            # Iterate over data.\n            for inputs, labels in dataloaders[phase]:\n                inputs = inputs.to(device)\n                labels = labels.to(device)\n\n                # zero the parameter gradients\n                optimizer.zero_grad()\n\n                # forward\n                # track history if only in train\n                with torch.set_grad_enabled(phase == 'train'):\n                    outputs = model(inputs)\n                    _, preds = torch.max(outputs, 1)\n                    loss = criterion(outputs, labels)\n\n                    # backward + optimize only if in training phase\n                    if phase == 'train':\n                        loss.backward()\n                        # clip\n                        optimizer.step()\n\n                # statistics\n                running_loss += loss.item() * inputs.size(0)\n                running_corrects += torch.sum(preds == labels.data)\n\n            epoch_loss = running_loss / dataset_sizes[phase]\n            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n\n            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n                phase, epoch_loss, epoch_acc))\n\n            # deep copy the model\n            if phase == 'valid' and epoch_acc > best_acc:\n                best_acc = epoch_acc\n                best_model_wts = copy.deepcopy(model.state_dict())\n\n\n    time_elapsed = time.time() - since\n    print('Training complete in {:.0f}m {:.0f}s'.format(\n        time_elapsed // 60, time_elapsed % 60))\n    print('Best val Acc: {:4f}'.format(best_acc))\n\n    # load best model weights\n    model.load_state_dict(best_model_wts)\n    return model\n","execution_count":19,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def visualize_model(model, num_images=6):\n    was_training = model.training\n    model.eval()\n    images_so_far = 0\n    fig = plt.figure()\n\n    with torch.no_grad():\n        for i, (inputs, labels) in enumerate(dataloaders['valid']):\n            inputs = inputs.to(device)\n            labels = labels.to(device)\n\n            outputs = model(inputs)\n            _, preds = torch.max(outputs, 1)\n\n            for j in range(inputs.size()[0]):\n                images_so_far += 1\n                ax = plt.subplot(num_images//2, 2, images_so_far)\n                ax.axis('off')\n                print(preds[j])\n                ax.set_title('predicted: {}'.format(make_list[preds[j]]))\n                imshow(inputs.cpu().data[j])\n\n                if images_so_far == num_images:\n                    model.train(mode=was_training)\n                    return\n        model.train(mode=was_training)","execution_count":31,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using focal loss function for better result\nimport torch.nn.functional as F\nclass FocalLoss(nn.Module):\n    def __init__(self, alpha=1., gamma=2.):\n        super().__init__()\n        self.alpha = alpha\n        self.gamma = gamma\n\n    def forward(self, inputs, targets, **kwargs):\n        CE_loss = nn.CrossEntropyLoss(reduction='none')(inputs, targets)\n        pt = torch.exp(-CE_loss)\n        F_loss = self.alpha * ((1-pt)**self.gamma) * CE_loss\n        return F_loss.mean()","execution_count":21,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# using resnext pretrained model\nmodel_conv = torchvision.models.resnext101_32x8d(pretrained=True)\nfor param in model_conv.parameters():\n    param.requires_grad = False\n\n# Parameters of newly constructed modules have requires_grad=True by default\nnum_ftrs = model_conv.fc.in_features\n# fine tuning last layer of resnext pretrained model\nmodel_conv.fc = nn.Sequential(\n                                nn.Linear(num_ftrs, 512),\n                                nn.ReLU(),\n                                nn.BatchNorm1d(512),\n                                nn.Linear(512, 49)\n                                )\nmodel_conv = model_conv.to(device)\ncriterion = FocalLoss()\n\n# Observe that only parameters of final layer are being optimized as\n# opposed to before.\noptimizer_conv = optim.Adam(model_conv.fc.parameters())#, lr=0.0, lr_decay=0.96)","execution_count":22,"outputs":[{"output_type":"stream","text":"Downloading: \"https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth\" to /root/.cache/torch/checkpoints/resnext101_32x8d-8ba56ff5.pth\n","name":"stderr"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=356082095.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b1a9840352644eba8bea5d2d0aceba0a"}},"metadata":{}},{"output_type":"stream","text":"\n","name":"stdout"}]},{"metadata":{"trusted":false},"cell_type":"code","source":"model_conv = train_model(model_conv, criterion, optimizer_conv,\n                         exp_lr_scheduler, num_epochs=100)","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"torch.save(model_conv, '/kaggle/working/data/modresn66')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = torch.load('../input/resnextmod/modresn66')","execution_count":23,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for name, param in model.named_parameters():\n    if name in ['fc.3.weight','fc.3.bias']:\n        param.requires_grad = False\n\nmodel = model.to(device)\ncriterion = FocalLoss()\noptimizer_conv = optim.Adam(model.fc.parameters(), lr=0.00003, weight_decay = 1e-9)\nexp_lr_scheduler = lr_scheduler.ExponentialLR(optimizer=optimizer_conv, gamma=0.96)\n","execution_count":25,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = train_model(model, criterion, optimizer_conv,\n                         exp_lr_scheduler, num_epochs=50)","execution_count":26,"outputs":[{"output_type":"stream","text":"Epoch 0/49\n----------\ntrain Loss: 0.6061 Acc: 0.7492\nvalid Loss: 0.8332 Acc: 0.6863\nEpoch 1/49\n----------\ntrain Loss: 0.5729 Acc: 0.7552\nvalid Loss: 0.8188 Acc: 0.6906\nEpoch 2/49\n----------\ntrain Loss: 0.5642 Acc: 0.7596\nvalid Loss: 0.8209 Acc: 0.6832\nEpoch 3/49\n----------\ntrain Loss: 0.5720 Acc: 0.7589\nvalid Loss: 0.8107 Acc: 0.6882\nEpoch 4/49\n----------\ntrain Loss: 0.5730 Acc: 0.7564\nvalid Loss: 0.8130 Acc: 0.6863\nEpoch 5/49\n----------\ntrain Loss: 0.5808 Acc: 0.7538\nvalid Loss: 0.8008 Acc: 0.6906\nEpoch 6/49\n----------\ntrain Loss: 0.5548 Acc: 0.7619\nvalid Loss: 0.8054 Acc: 0.6875\nEpoch 7/49\n----------\ntrain Loss: 0.5758 Acc: 0.7581\nvalid Loss: 0.8004 Acc: 0.6894\nEpoch 8/49\n----------\ntrain Loss: 0.5345 Acc: 0.7672\nvalid Loss: 0.7951 Acc: 0.6869\nEpoch 9/49\n----------\ntrain Loss: 0.5422 Acc: 0.7716\nvalid Loss: 0.7998 Acc: 0.6888\nEpoch 10/49\n----------\ntrain Loss: 0.5539 Acc: 0.7556\nvalid Loss: 0.7938 Acc: 0.6906\nEpoch 11/49\n----------\ntrain Loss: 0.5632 Acc: 0.7691\nvalid Loss: 0.8073 Acc: 0.6937\nEpoch 12/49\n----------\ntrain Loss: 0.5339 Acc: 0.7649\nvalid Loss: 0.7957 Acc: 0.6863\nEpoch 13/49\n----------\ntrain Loss: 0.5284 Acc: 0.7731\nvalid Loss: 0.7971 Acc: 0.6912\nEpoch 14/49\n----------\ntrain Loss: 0.5735 Acc: 0.7549\nvalid Loss: 0.7903 Acc: 0.6931\nEpoch 15/49\n----------\ntrain Loss: 0.5372 Acc: 0.7693\nvalid Loss: 0.7797 Acc: 0.6931\nEpoch 16/49\n----------\ntrain Loss: 0.5281 Acc: 0.7652\nvalid Loss: 0.7883 Acc: 0.6931\nEpoch 17/49\n----------\ntrain Loss: 0.5302 Acc: 0.7664\nvalid Loss: 0.7841 Acc: 0.6894\nEpoch 18/49\n----------\ntrain Loss: 0.5346 Acc: 0.7656\nvalid Loss: 0.7889 Acc: 0.6894\nEpoch 19/49\n----------\ntrain Loss: 0.5283 Acc: 0.7673\nvalid Loss: 0.7846 Acc: 0.6918\nEpoch 20/49\n----------\ntrain Loss: 0.5381 Acc: 0.7675\nvalid Loss: 0.7900 Acc: 0.6918\nEpoch 21/49\n----------\ntrain Loss: 0.5303 Acc: 0.7690\nvalid Loss: 0.7818 Acc: 0.6918\nEpoch 22/49\n----------\ntrain Loss: 0.5554 Acc: 0.7647\nvalid Loss: 0.7896 Acc: 0.6863\nEpoch 23/49\n----------\ntrain Loss: 0.5315 Acc: 0.7722\nvalid Loss: 0.7833 Acc: 0.6918\nEpoch 24/49\n----------\ntrain Loss: 0.5104 Acc: 0.7724\nvalid Loss: 0.7825 Acc: 0.6912\nEpoch 25/49\n----------\ntrain Loss: 0.5150 Acc: 0.7753\nvalid Loss: 0.7756 Acc: 0.6931\nEpoch 26/49\n----------\ntrain Loss: 0.5371 Acc: 0.7670\nvalid Loss: 0.7796 Acc: 0.6949\nEpoch 27/49\n----------\ntrain Loss: 0.5386 Acc: 0.7711\nvalid Loss: 0.7781 Acc: 0.6961\nEpoch 28/49\n----------\ntrain Loss: 0.5162 Acc: 0.7788\nvalid Loss: 0.7768 Acc: 0.7010\nEpoch 29/49\n----------\ntrain Loss: 0.5288 Acc: 0.7776\nvalid Loss: 0.7823 Acc: 0.6974\nEpoch 30/49\n----------\ntrain Loss: 0.4973 Acc: 0.7805\nvalid Loss: 0.7778 Acc: 0.6955\nEpoch 31/49\n----------\ntrain Loss: 0.5367 Acc: 0.7756\nvalid Loss: 0.7819 Acc: 0.6992\nEpoch 32/49\n----------\ntrain Loss: 0.5369 Acc: 0.7678\nvalid Loss: 0.7760 Acc: 0.6949\nEpoch 33/49\n----------\ntrain Loss: 0.5376 Acc: 0.7768\nvalid Loss: 0.7780 Acc: 0.6961\nEpoch 34/49\n----------\ntrain Loss: 0.5154 Acc: 0.7781\nvalid Loss: 0.7810 Acc: 0.6924\nEpoch 35/49\n----------\ntrain Loss: 0.5337 Acc: 0.7690\nvalid Loss: 0.7808 Acc: 0.6918\nEpoch 36/49\n----------\ntrain Loss: 0.5215 Acc: 0.7753\nvalid Loss: 0.7802 Acc: 0.6888\nEpoch 37/49\n----------\ntrain Loss: 0.4987 Acc: 0.7870\nvalid Loss: 0.7766 Acc: 0.6912\nEpoch 38/49\n----------\ntrain Loss: 0.5070 Acc: 0.7748\nvalid Loss: 0.7745 Acc: 0.6943\nEpoch 39/49\n----------\ntrain Loss: 0.5083 Acc: 0.7773\nvalid Loss: 0.7737 Acc: 0.6949\nEpoch 40/49\n----------\ntrain Loss: 0.5042 Acc: 0.7782\nvalid Loss: 0.7851 Acc: 0.6906\nEpoch 41/49\n----------\ntrain Loss: 0.5123 Acc: 0.7776\nvalid Loss: 0.7772 Acc: 0.6931\nEpoch 42/49\n----------\ntrain Loss: 0.5173 Acc: 0.7822\nvalid Loss: 0.7787 Acc: 0.6924\nEpoch 43/49\n----------\ntrain Loss: 0.4996 Acc: 0.7811\nvalid Loss: 0.7740 Acc: 0.7004\nEpoch 44/49\n----------\ntrain Loss: 0.5270 Acc: 0.7736\nvalid Loss: 0.7749 Acc: 0.6949\nEpoch 45/49\n----------\ntrain Loss: 0.5150 Acc: 0.7797\nvalid Loss: 0.7809 Acc: 0.6918\nEpoch 46/49\n----------\ntrain Loss: 0.5418 Acc: 0.7698\nvalid Loss: 0.7781 Acc: 0.6992\nEpoch 47/49\n----------\ntrain Loss: 0.5059 Acc: 0.7850\nvalid Loss: 0.7763 Acc: 0.6943\nEpoch 48/49\n----------\ntrain Loss: 0.5127 Acc: 0.7774\nvalid Loss: 0.7718 Acc: 0.6974\nEpoch 49/49\n----------\ntrain Loss: 0.5064 Acc: 0.7822\nvalid Loss: 0.7743 Acc: 0.6943\nTraining complete in 51m 13s\nBest val Acc: 0.701044\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"visualize_model(model)\n\nplt.ioff()\nplt.show()","execution_count":32,"outputs":[{"output_type":"stream","text":"tensor(31, device='cuda:0')\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","text":"tensor(10, device='cuda:0')\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","text":"tensor(12, device='cuda:0')\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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vR7FZMJkHBFPJlhb4vmaxfklClPp4KAdUmoBFKQqRAY5x0+d5omnXsAKwc72Duu3bpInCZ1Wi4PL89Nk9XSB/eiLz/HCs0+ipEIrReD7aK1ACrQSYC2lNYyiiKeefp7Ll2+xsvIFtPLwtU9epBx5+CDf+6lPYJRkmJT0ohLtBRyea9NsSoq8YGd7n52dXaI4p9lo02520cJi8pwszbDGIpVESEdpNXmeE6c5OMODj5zm1d/dII5S0jgmixM8rXnmued48cXvodVu4qO4cv0yv/aZ/xubWzozHR66/xGOnjzN9t4Wa6u3iEYDOo2ATiNgpjuDE2qarJ4usI1A0fAbOAvWORAGay1SAE6zuz/g/JUVVjZ2ePviRW5uj5g5cJoyjWk1NCeOn+YDz77AZz77OZ547llEq41wOYG0uDSnH+ckWUGalbQ7c8wuambnukjnSCYR8RDKOMU6h3LgrMNvKJSvcaLEWZ9+f8J4OGYyScjzCGEKnHNcX93m6s/9Es4UeFIjhMWWJafPnuHwwmGcsZx7+w16/V0wOQ1fMz/XYTScYEwBojFNVk8XWGtKrLMIJA6wTuAQ2FyQuxSpJAvzs2SlQwvN6WNn0Nrj8MElHnnoDMOkoBCac+fe5jd++dc5cfoUly5fZG99lVB7qFCTFgWtZpfDJ0/xwOOP4XUaSK0Iui18IVDa3ZmxTkCjFTDTaTPTbPD5L77Jl776OlmWkcQxk3GPLBljSsPuflRt7+UpKEUaDzl5ZJHTpx8kHg55/fUvk6URntB02m2ajQbWCLrdWbSn8Rv3MLBCSJRQCKq9T+ccQoBQApyg3fI5e6rD2ZMnqjQFTzM3N0en00ErxcZ+n6tr22jfI2x02FjZJLA+c+0FRpM+Oxt7jEZj8jTh/Dde47VXf4fjZ07zgRee59DxY2g/YH6ug7WmsoYdoATtVoNOs0HLV9yaTHCZpShismhEEscANDxF6QQOjdQeJlUII8iSknNvfo1kMsT3fFotD4kkSyEIFTrUlFZx7fqtabJ6+sBKKaninqn/73CARCEAIQRSKjrdDjMzMyjl4YDSWsJmg/tOHefksaNsbuwwHieM44i19VV2NnfZ3+0TFxlpHpFHEVEUsb2yw2f3P8PigWXmDi4yNztDZ7ZL0G7hhw2kFuztbNMXin5vn7LIybOc4bBHmcQ8ePYMveGQ/dEAaw3alTghoIjJM4+3vvZFhnu7+J5PM2yRpwnOc4Shj1ICYyX9wYTd7Z1psnq6wFagVuBBHaxeHbkDahCGzM3N4vkNLIKitOTGUlhLf39EXhg6szMcP3EYJcAYKJ96lCiJuHj5BpcurzIYTxiN90gmEa4s8X1NUZRsXFlnV2+xeGCJcRYjtabZCGg0AtLMsLXVx8eRR0PyaIRy0N/bZZJMKOMCZyyFE2jfpzPXoSAn39tAS0mz2UYIhXACrT20dgjhyFPD7vYOStzD69gqBMVWflkExhikkAghEErRbnfozMxigElmMdZRGkfpHHlhcNqj3WgCjjgpiHOLdIaDc01mOwvYUrB46CRJYSjSnNF4xHA4ZDIZs7e3C2WGMAYpwdOSMs8IjCMwYEtHVymyJCIdbZMM98iSnK2tPq22z6NPfJD5IydxsolCcOvym/S2bt4eFJNoQhj6hIFCqxClFUprosGIJB6g9XSjQacMbDU4Y0qkVGitEELheQHNThcdNBjnjtw6rDVYY7HWkeUlDvC0Jsockywny1IaHsw0G/SjnGiYsrY3QiuPZiPA95sE7ZC55UWKouSMsRR5iSsKnCko8hRRFOAypDSkUcali1d4/eZ5ev0BcRwjjAVhefy5l3j4iY8xzg0mT7h5/jX629dxZYoTCk8pAl8hpcMhcc4gRYBSmp2dTZwpMVPbYq9oygHj1eikBCFBex7tVge/1SUpYRDlpKXBGIfEIIAyL3FCUjpIoow8S7FZjh8ExM5jkMaUCKx1OOVTOBinJQDWVv5+XCUVpC+QvocnHVq0Ec5i04yN1RUuX7jCzv6Ag8fvY+7gUa6//SbZYItJ6jCiyWAyYdLb4uaVtxnvrlLmCUJ5+HVslAKEA6zA8xW+r8jzjMFgQLvVYrqbdlMHtpqxTniEjSatdgetfYZpyWBSEBcl1hmccWAsFocTgiTPSJLqTzhLI/DJMRSZxTiJsbWYcw4pKnHvrMM6AfUMcs4iUHjC0mhIlrst3nrjPN946w26nQZPPfMY11a3WdncRZkO3fmD7Ax30NLDpikrl15n7doFsmiMlg4lNZ5SSCFACJTWtNtt2u02s7MzzM3NsLa+RVkapBZ023NT5fSURbEB6aGbcyhfUzpFP87pjXOSpEAIiVCQpim2EBTOEmUpeZZTpDlKSHQQ0E9KLAbpAAdC1pJACJytnB4Wi7OiXl5VgHtaoIVgtDNksDpmMurxiZc/TLPV4uKNFXb3+wgkSinmFpbZualQwrJx4yJxMoYyQwJK+mip8L2AIPRotVoopbDWMh6PyfOcXm/ApYtXUUrRbDf54AsvTJPVUwbWgvICsrJE+ZLhKGJnZIjzEuEc1lqSNKW0BUXhKIuCNEooyoKg2aCQCpIca6kdHA4wlfEFFYDUSyYEQjikEoSBRjjDxq0V+jt7vPjcMxx78DiltGxt7vH6W+dZ2xmQlwJrDNZZGq0mswsLmDxmPNhHSYeUGqUkfhgShiFaa6w1jEYjlKpshmazgdYKY2E8ifB9DwMcO35qmqyeLrBZaTCuIOw02J/k7OyPGUemEpsY4iQljjOKMqc0JSYvEA7CZoMkzbEInK0WR67+rwLVgXBIXBUjLCXaUzQCD60cW2s3WVu9xcGlZV55+cMcXpwhiVPOX13nzavXKbGM85QkzzFFTjzcZe3qRcb7G+AMUmikkEghCfwQax3jyRgpBZ728DwPpSTgkEKgpKI/HGExeApOnDxNVtzDoTFREuPNdNgdxKztjhhNEoo8w0OQ24L9/pC8cDhrwFq076E8TTqJUUJV01Q4nHQIHFqIyosFVaySqnSdpwRKlGxtbLO+ts783AwvPPcCh5fmkNJxc32Pty5e5cbWLuAweUkyycjTmJ2Na2yvXsIkMVKUIGo3irNYW5CmFiEFWimkVHecLkpKPK2RSgGSXm8PZwu8RsDhw4fI8nyarJ4usGk0JlYdbm5M2BmPUXkBGCZlyXAck+YFZWkQzuH7HkVZ3GFc5cCo/pFSopUEBZ6n8T2P0JcEvsbzNPt7e6zcXKHR6vLw449zYKHL4aU5ZsKAt6/d4KtvXqQ3SChMSVHkZGnKZLjH5solxr1NXFlW/mwnKuMIi6NebwtQogJSqtuAKrSW+L6H52myIiNPU7SsDTtj6HSa02T1dIHd6Y2Ix461nZQsywmEQSIZDAYkaUZpHMYYlFQUuaoYKavAMyVqRqp6ZviaMAjotjxajQDf1wyHI94+fwOBx/33P0K726LbbdAMNaGnuHTzFr/7lTcYRDF5UZLmkMdD9jZv0d9awSYDpCsx9RablArhHM6BlAKtJVoqtKdRUqK1QnuyOq41UmnSNGN9c4X5+XlKkyH9NvPLJ4mSe1gU92OLdT3SYUKWOSKTUuSOLMsorSUvSgQCrSRCuNpvLJCiAlRrBZ4ikAEaEEZQpoJxlrKyvsYwSjh2/DSd7ix+oAg9ichz9sZjXn/9HNv9MfvjlCzOsUXKeLjH6vWLCJNz5MhhCnsYkyck4z6T0RApQMhKYnieRutqhipZzd4KVFG9cFpRFAW3bq2ytDxPGMxQbq3TbTY5fOwo62v9abJ6usCqmUP0r76BlxZs7ycU1lAaQVkaSmuqzQApkU4gpUNJgRICqQRSawSawAtQ0qFkZf1u7uyys9tjZmGR46dPVqGkSqCkIY0idgYjbqyuE+cFZVYS5TlJMmF/7QZ7GyscPnaMhx57ijRNuPDGa2TxmFYrpAg8cA5BFQ+lVSX+pRBoWS2JpFL4nofSiizP2d/vs7Awx+zMArs7A0zpwBYMB7usr93DuzsRIV967RucOjhHb2AwZYkTCmsl1CK3qngCUkm0UvieBiNQ1iBcUXmknCFLE4ajEVJ7LB89Rhg0UEDXV3iUpMOUta3tKjC8MJTGUqQxo719Nm6eJ81GPPzk85w6dT9vfP33uHXlAhJDp9MmnhT4nkfg+cRxVEmN2ypBKrTW9fKmEslZlrG336M7M8Pc/CyD4ZCt7XXiSUzYjIgGQ+Job5qsni6wc02PwbjH56++yeLyWRpzVZhJkhrywoLUOAFKKYRSlLWu1VohpSQXkKhK9yIVC0uLdLszaClo+YJuy2c87LO632N7f8A4yXGminRI4pjt9Svsba8QSMups4+xtHSQr7z622xv3MKWGX4YYEyBoNLfjUaDosjrZZWsnBdSInCV7leSOMnoDwbMzs4xM9Ol3+uzurpJb38IwpJnCZNRRL+3O01WTzfb7te+fNNdOvd5fubv/m3KFJ545lnmFuaIRiNG44T9wYSsMEjlEXgaT3soz0dqDyHlndnS6XSY6c7gBT5+4IGzZMmEfq/HeBSjPB8hFc45CmPob92it3GNPM8JtWNm+RgnTj/ApXNfZzjYxZYljYaHp73KCSEFSkmajQYCh7GmEsVS3pmtUmuSNGV3d4/l5QU63Q6jccLG+g6DQZ8sy1Ha0Wj6HDl+lla3y2d+8zfvzWy74TDm9H1P8IlP/Xk++1u/wQP3PcCRMyf4hX/0DwiDFi1fU6YFSZIQU+k3pRRKeyjt4/kerWaTLB6zuXqToiiw1mBsSZEXeJ5P2OwQhA2UBlfmjHY32Fq9iqcFoeehvSYLB09w+cJb9HbXEFLSaDTQSpJmOWURo7RiptMGYZFS4KCyxj2N5/k4BJMoYr/XY2lxgXany2AYsbG1w2A4rtbhOKyFNDVsb+3w6PLBabJ6usAmxnLlzbd54UPfx+zMQXxPMi4K1lY3sGVJEDSQOkCqAKc1FrAlFHlaBWULwQDqtSV3lkOVw7hac6ZphHUlh5c6LHiSnm0y2AmqgHAc4dwyOzub7G3dQGBpNtrgHP3+gLIskVJijMGZklYrxPM8hLO1pS4xzjEYDphEE5aXF/G0pt+fsL3bZziMKg+Y8lAoHAKpA+bmDnBgaXGarJ5y7s5+j+uXzrNx7SZnHj5FEWeg5/AaHZJRjyQeIpwAoTE4pNIIVc/CZgekwtnKbXcbVCcE2gtoNZvMzc5w8swpDi8tMLhxjmy4R57ECCWxGNAthNBsrV7GmIJWo4EtSwbD4R1Qqd2Uw9GYpcU5Ql9XlrpSGKC3u8dkMuHIkUO0m236g4jd/X3GUYTSEi09pPJBCvwgZGZmlkOHlnnyyYenyerpAltkKdcunWex3WE02qUoCz7+p3+YH/zRH+fVz/0Wq5ffxsQRwjkUlrIscHnBQ089xaPPvIizBdFwQjNs0p2bwQ8DHApaM7gsRnmaVifk5GyLL13+CrGQbOwNkM5ghEQ3OsSTPiZPCPwAqTxG4xEIgfZ8rLGVVS4FxhqiJCZsBHhS4Szs9XpkWcKRQ4doBA36wzH7/TGTOEZJhef5+H4DFYRoz6fZatFtdzhyeImHzp6eJqunC+xMq8GpBx/hC//yV3ni6ecIteBz/+L/4OzZh/nLf+XH2e7t88ZrX+bqpbcZbG1CltMOPR567FHmFufZ2bhBoFIa0vDA6dPIRodhVNJdWMZPx2z3hownI4b5AGtLJmlGkiRVRJX0ETgGvT1wjsCv0h0diiD0kUphTUmaJpVkR9Tt4IRkc2sbYw2Hjx6m4Yf0+0P2ByPiOEMprwoxDZp4XoAXhgRhSLPRZnGuyUc+9AHmujPTZPV0gdUm50/9mT/Lxde/xM6t63zgQ88zGk7o795k7dYl/KDNiaPHefDRJ/F9n821W8S9bYTXYDIaoB2cPHWK3u4WWVZgTURROlpNnwMHTtGeGbKzv834xnlya7m5tlkFawMiaJBFE7RwqCDEGihLW61/tQbn8DyNkJIkihAIsjSnyA2jJAIhOHToAJ726PWH9AdjJklWe6U8pPYIwibaDyr3o1R0GpoXPvgoZ08cu2MXTI3X0+xs5cZ1Tp7+CD/843+D3/qlnyPQgsvnv0E8yTl9+hTLR4+wduMCZWpJ0hypJSeOH2AyHrGxeo0zR45x6KUTkoUAAA4HSURBVPAR7nv4UcapIS8y5ppN2jPL7MQWU1qksMRxxP44Zr83wGFB+kipcCZH4fC0Jk4SPM+/s6eqlARn8T1NqRWmqEJriqLE931muh2kFPT6Q4bDSfV8qtK/KEWz3cUPGnieB66gHfo89vAJ7j9zEmcs4l4OZitNyav/6ku8+OHv4d/90b/KlW98AaRkNOizsaFZ2Vojyy1LiwfptBp0uouM85LF+aOsrVxn6amDNFptrDVEcYJB0ZA+e9ubDMcRo40reEpgspL1nQFFniOEwyqNKVN8JdCBXzkdHCjtYU2B72s8r3L8F3mJ73lkaQo4gsCn1WzgHAyGEcNJRJoVKO2hA58gbCCEotFo4vsBUkpm2m0+8cqLPPn4g4Q6ACcwU65ZOVVgt29eJZw/yGd+61/wwWef5LHnP0qsOnz2N36JcW+P+bllGjMaUSZcev1tlg4f5ZmPvszswixnH7if33n1Va5dv4LyNGce+SB+e4GN3SHSpWBLdNAhi2POXbnC2uY6xpQIBcrzkMbgXBVtkeUFUmuMMwSe4tiJo5w+doybN1e5tbaC0nftLEkw1jIexYwmMVlRoJXGD0O8MMQPGwR+myD0CT2f0NecOrbIA/edpNVoIJ3CWlfv006Pprttt3aJVjRhMneIX/31f8kHnnyMV175Ph579kW++Jn/h3O/+1l2b91EKMvBoyfodmfY3NglaC2yMHuAaCmiNxhz4Ohx2stHkEEb34IUYPKMPMuZJFe5trZOnqVIJRDSqyIuTAGu2lc1xqFVlUF08MAhXvjIyzx48gQnb67wy7/ySwyGVSk/JRXGlPQHGZNJWme+V/HCWgdoHeJ5IX6jcj82PMXJo8u8+MJTzHS6WFPFRvtegPLu4fxYP2gQxX2KLKEze4AvfOE13nzjLZ576SO88qm/wHMf/3e4dfFNdq6fx2Y5X/3yl7m+tsdb5y7hSfBDjdaaazd3eO0b15HaR1CiBGR5SZ6PmeytkvW30UrhKFFeiBAaYwukVGRZXulF52i1qpSM3iDCSoWxlsOHDzMYXqtnrCSKU6IooyhdlU/r+WgvQPtBrVMDtCfwtObokQU++fEXObCwWGW/mxKErKIty3s4E2B7Y4vm/CLOk9itm7TnF+kNS37l0/+QMAw5/eiHOHD8GNZJvvLVr1f7swJsmZIhyPKsDlSbwP4+YHHOApVXSLiYPOqh6ngo6yxB0MAUZRWCSrUTo5RGAMvLS/R2Nzl88ABJPCJOxszOdBHC1jtNgvE4pjSunqEenh+ivKDSsVrj+z6B9pmfafE9Lz3L0vx8HcbjaoeHrJdM0+T01KMUc8abt+guHqLwPYZrK8wvLBLOtNhZX+Xmb36asBVSGIWwCufE7UjkakfFSRyCUliEMBWzrEO5AlvEmHiEtAYnwVmBQyG0RxGPEK5yOlhr0FoRhgHPPPsBvvr1r7G0sMTu+lUmWcns4sE7pYGMcThsPUt9PD9A+407UYphGBD4mrl2yEvPPcaJw4dQQmKdreOYq1gpRJVkPU2a7jrWWYQPyWSbzGgazTYrl99ibmGBAwsLZN05hlFBbhOEs1SFO6iD2MBhcQhwFmVcpTNNQZGPsGWKsFT7u86hsQgVIITDlBlaCMqivBODfPDgQU6fPsnM8kG01+LS1VssHD/JTNisIvqpjCelPLSugQ1CgkaToBEQhgGhH9JseHzgyft5/OEzKHSVyC0VztXhsa6Op7yXRbEpc2SgMc5SJkMm8ZDheIzJY4bBPkGzg3MSH7AKbFnWAAOIagNeVBVasmSCKxKqOi4O4W5v1JdV3KKoSiH4lNWCVEmytABZ5QudOnkS39PMz83R293ihe95mQOHj/LVz30eQVX5RXiVoeR5Pjrw8MIA3/fxvCrktBGEPPrACT74zJM0wha4SpdbZ3G2zi4UYIz5/T8B9cdMUwV2PxozF8wCEAYBZV5W4spa8nRCloyxTiCkBnU73LRaAyolkdZWxaPLElPmYAtQCiFVLfaqkjxSCCyOIs8Z9PpY67BYjDFo5VcVY5xjMJxgnMOTPiLTDNevs7+zgnG2MpS0h+cH+EGIFzbx/BDP9/C9EF8rjh+Z5cPPP0m7EdQJZAVSV7m+TtRKpNa1Ut7Dy51GZxZXCpSoc2SVBCkpnUAKh6N6s21pobR38medACuq66gkcBXsplQ1Eaytzqsz5B2VCEySGKjCWgwC66pErzAI2N7Z4fK1Wzxw/ymyNAPn0O2Q8WQMWJSqMtd9v1FZv36I71eeKq0kBxZm+dhLz9JpBjhTBag6RBXnhKnSTVwd0D5ldyJM+TcBDh8/Q+kUpgRjHU6A5/s02h100MAJD4vEYRGCOtbodupGBbKlAtoJiRVV2t63SrnbM6UOsalfoqIouM3fIAxI8oJDJ+5DeW1Sa1nZ3GOYe/RGKcLJaqb6IVJ7IAVKVUHinta0Wx4HD8ww1+2gVYCUGlvn/lYPR7V/jECIyjK29h42no6ePEuc5eyv3UKZEqWrYlpZltFqtpBo0mTC7fIFVZyRQ7gqQdrVmeM4SVEWNBoBWgj6vX2crXSxc7evdnVMcDVrytLUeY7VS5Fbx9kHH2H95k1GgyFFsEBz6QT7vQFCVcsaL2jUAPu1Y1/QCn2efeZR7j99hDAI7gQAIEqMMQg0Suk7/Vbg8s3qDFOi6e7HpjnHj52iTBN6OxvorKrl7sqMNLJIP0QGDWwSISjvLBWsdRhT0p1foNFZwggPkcUE2pKOh9jSVs5+KoPldrTjbeY6VwWiV0VMLF6jzaf+3A8SKokWluPHTyIPPMDuxjbDvT7C13h+iPaDaonjaRpByFy3wdOP38cDxw+yMDNb596WiOJ2YnsForWVPvc875uDv5eNpzieIJGcOHEWZ6C3s05pDCaKCIIAaS3aD3FSYkpbh7uA1hpjDEUSU2QbIDyyLCKyZVV1tF4TiTodAyRSgjEVNytGWzwZ4ChZXFzihWde5K3Xv8Te1i3OnDmD9ENeu/QGpkxotWbuWL5B4ON5inbT4/lnH+fpJx7GVxXbBFW2gLW1Q8NWeUVVOQZRpXPaaac8VzTd3J0kJo0T2t05jp+6DyFhf8eRjYfEaYJvLILKlVfNVAvYO7Muy1KEy+9o20qvAdxe7FbfhXAYUzG0uraue+EsxkEUjbh0/nWuXTnP9uYGRw4fQg73ufz2a3iexA9aaK9yGQa+RzP0efrJh3n84bN4SoIVSK0Q4s6vRQKVtLidDuLqtNDbUmPaAE/X82QMQkK/t0u32+XEqVMoT9Hb3mI06pHlOUoovEYASqOkxdVvvTEG6iTmOju2zrT7pvK6zWR7R99W34uiqK4RJTifUX+Pzc3rnL90hbn5RYajmBvnfofttRU6rRY6CCodqzWh73HfqaM8+cgDhIEPQGkNorBIC1p5d168NE25bY8GQYAxVRK2Uopp/9TcdCuzIXDGQpHQ3xlTZPMcPXaGsNHB215lsr9LmeUoU635rHMIKqCKogAFx4+fpN/bJ0kmYAxKKW7Xi7qbbs+UoihJ08o/7FAIB/F4zO7uLmceepylA0fY2dniza9/CVcU+GEH6SmkFoSh5uSJQ7z0oadpNX2cq6zsosgIgrAuYlSpCudcvWnvkLLK46lmsKMoiqkDO9XlTlkarC3rH1Mo2N9aZ/PGFeZn5zlz9hGWj56mOTOPLR0KXZ1/O6lZaaRULC4u0p2dqQLCqYwq9y319mVVe0LIepFEveSgdkcasjTmxrVbHDpwmGajzaVLl9ja2iRoNvEaAYEXEHia5YUOL33oKbqtFphq5kdRXDscRA2kJYoirHVo7eP7QRVUrhXGWYyt8pLslK2nKW8CVEU/jLV1uZ+C/v42URRx6OgpTp95mN3uLOurVykn42q5YgxSKjwtMU6wunqLMAwwZVEXf7Y4p6rEZFeJW6WrLAJrTLXMqUnYyqlvSse1yxdJkozCOLbWVrCmpN2eIQya+DpgYa7BSx9+mgMLi1WSmFfr89rCvlvE3tanxlh83689Y4bbSb3WTNdPDN+FymwlVfn1ijmVazAa7nEzTZhZPMCBQ4c4c//DXHr9qyjPI01SwoaHFApbGvq9HjiLLetoe2PJsxKhBJ6v0b6PtY4kTSrgjb1tYVVhMraKiEiTmFvXL2KspSxL2p1ZVNCoCk2HkmeefJRjB5cxWUJuBc1Ws8ohUpI0SQHqJY1PEIQYY7E2x1qL1pqiLO6sZ6W8XWpwejTdmKcyuzOzbrsAEWCdIYl7xLf69HfXWVo6RKfTZTwZIbWhNA6lBEVZIrBgbS1eqxjgRitEiCqmKk2S6r71hsG3lPcTlberLE29ts0xztJqz3Dg6HF00ACXc+LIPCePH6LMLTqQGGcpyjoRSyqQCmSVFaD5pvehSuKqoiLDMCQviipDT6naFpgeTdd4soaiDgd1CJyQtdem2p5zpiQe97kVDQmDBmWe4deGiSkzlJTgbi81Kg9TaQx5WeBMVbTrdsUYpLgzSypQubNBYMoSawRISavT5cix03S68/i+4vSxBV545jHKPCZXEus0UlS/hlXt5eo7PuNK1JdV+GltKN1Z09Yz9Y5Ff09bxfZ2fcTyzqz6ZqFMWbn8rMGUBVGef8taVNQvgK02OoFaT7tqs1aIb87Q296n29fWH2odb0BJlPLozMyzfPAwjVYHKQShH3Dq+GkaYRfjqh81dLZASkEQ+vUatapiftvKlaLeNdKasizwPE1ZlgglETXIUkqCIJwmq9//Ke97ld7/xed7lN4H9h6l94G9R+l9YO9Reh/Ye5TeB/Yepf8PHFXEG3+FqQEAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","text":"tensor(22, device='cuda:0')\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","text":"tensor(6, device='cuda:0')\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","text":"tensor(23, device='cuda:0')\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.eval()","execution_count":33,"outputs":[{"output_type":"execute_result","execution_count":33,"data":{"text/plain":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n  (layer1): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (2): Bottleneck(\n      (conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (2): Bottleneck(\n      (conv1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (3): Bottleneck(\n      (conv1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (2): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (3): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (4): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (5): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (6): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (7): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (8): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (9): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (10): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (11): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (12): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (13): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (14): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (15): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (16): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (17): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (18): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (19): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (20): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (21): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (22): Bottleneck(\n      (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (2): Bottleneck(\n      (conv1): Conv2d(2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n      (bn2): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n  (fc): Sequential(\n    (0): Linear(in_features=2048, out_features=512, bias=True)\n    (1): ReLU()\n    (2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    (3): Linear(in_features=512, out_features=49, bias=True)\n  )\n)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"torch.save(model, '/kaggle/working/data/final_make_70')","execution_count":60,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from torch.autograd import Variable\ndef image_loader(img):\n    image = data_transforms['valid'](img).float()\n    plt.imshow(img)\n    image = torch.Tensor(image)\n    image = image.unsqueeze(0)\n    return image.cuda()  #assumes that you're using GPU","execution_count":34,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from PIL import Image, ImageFile\nnum_samples,all_preds = 8041,[]\nout = open('result.txt', 'a')\nfor i in range(num_samples):\n    filename = os.path.join('./data/test', '%05d.jpg' % (i + 1))\n    bgr_img = cv2.imread(filename)\n    rgb_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)\n    im_pil = Image.fromarray(rgb_img)\n    \n    im_pill = image_loader(im_pil)\n    preds = model(im_pill)\n    \n    class_id = preds.max(1)[1]\n    all_preds.append(class_id)\n    out.write('{}\\n'.format(str(class_id + 1)))\n    \nout.close()\n","execution_count":35,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"labels = sio.loadmat('../input/devkitt/cars_test_annos_withlabels.mat')\na = []\nfor lab in labels['annotations']['class'][0]:\n#     print(lab)\n    a.append(convert[lab[0][0]-1])\nactual_preds = np.array(a,dtype=np.int);\nactual_preds = actual_preds.squeeze()\nall_preds = np.array(all_preds)","execution_count":36,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import classification_report","execution_count":38,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('accuracy = ',(all_preds==actual_preds).sum()/len(actual_preds))","execution_count":41,"outputs":[{"output_type":"stream","text":"accuracy =  0.680636736724288\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import f1_score\nprint(\"f1 score {}\".format(f1_score(actual_preds.tolist(), all_preds.tolist(), average='macro')))","execution_count":58,"outputs":[{"output_type":"stream","text":"f1 score 0.6892359828413914\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(classification_report(actual_preds.tolist(), all_preds.tolist(), target_names=make_list, labels=make_list, zero_division=1))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Test"},{"metadata":{},"cell_type":"markdown","source":"### Preprocess for input image"},{"metadata":{"trusted":true},"cell_type":"code","source":"final_model = torch.load('./data/final_make_70', map_location='cpu')","execution_count":63,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import ssl\nimport base64\nfrom PIL import Image, ImageFile\nimport http.client as httplib\n\nheaders = {\"Content-type\": \"application/json\",\n           \"X-Access-Token\": \"yrkuYbYWugkjcM3tfpO4ffCGHHOYgaJehWOD\"}\n\ndef plotting(path, x1, y1, x2, y2):\n    src_image = cv2.imread(path)\n    crop_image = src_image[y1:y2, x1:x2]    \n    plt.imshow(crop_image[:,:,::-1])\n\ndef get_box(path):\n    \n    image_data = base64.b64encode(open(path, 'rb').read()).decode()\n    params = json.dumps({\"image\": image_data})\n    \n    conn = httplib.HTTPSConnection(\"dev.sighthoundapi.com\", \n        context=ssl.SSLContext(ssl.PROTOCOL_TLSv1_2))\n    \n    conn.request(\"POST\", \"/v1/recognition?objectType=vehicle\", params, headers)\n    response = conn.getresponse()\n    result = response.read()\n    json_obj = json.loads(result)\n\n    if 'reasonCode' in json_obj and json_obj['reasonCode'] == 50202:\n        print(json_obj)\n        return 'TL'\n    if not json_obj or 'objects' not in json_obj or len(json_obj['objects']) < 1:\n        return False\n    \n    annot = json_obj['objects'][0]['vehicleAnnotation']\n    vertices = annot['bounding']['vertices']\n    xy1 = vertices[0]\n    xy3 = vertices[2]\n    return xy1['x'], xy1['y'], xy3['x'], xy3['y']\n\ndef crop_car(src_path, x1, y1, x2, y2):\n    src_image = cv2.imread(src_path)\n    if src_image is None:\n        return\n    crop_image = src_image[y1:y2, x1:x2]\n    dst_img = cv2.resize(src=crop_image, dsize=(224, 224))\n    img = Image.fromarray(dst_img)\n    image = data_transforms['valid'](img).float()\n    image = torch.Tensor(image)\n    return image.unsqueeze(0)\n#     return image.unsqueeze(0).cuda() # if cuda","execution_count":69,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def predict_make(src):\n    resp = get_box(src)\n    if not resp:\n        return \"error\"\n    plotting(src, *resp)\n    image = crop_car(src, *resp)\n    preds = final_model(image)\n    return make_list[int(preds.max(1)[1][0])]","execution_count":70,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import json\npredict_make('../input/stanford-cars-dataset/cars_test/cars_test/00001.jpg')","execution_count":71,"outputs":[{"output_type":"execute_result","execution_count":71,"data":{"text/plain":"'Ford'"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 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